


'''simple docstring'''


import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class        lowercase__    :





											'''simple docstring'''


											@staticmethod
											def UpperCAmelCase_      (       *__snake_case       ,   **__snake_case    ):
															pass







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= hashlib.mda(image.tobytes()	)
				return m.hexdigest()[:10]







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Any							= np.array(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Dict							= npimg.shape
				return {"hash": hashimage(SCREAMING_SNAKE_CASE__	), "shape": shape}







@is_pipeline_test
@require_vision
@require_torch
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							A_				: int       =    dict(
							    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
							A_				: Dict       =    dict(
							    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= MaskGenerationPipeline(model=__snake_case       ,   image_processor=__snake_case    )
											return image_segmenter, [
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											    "./tests/fixtures/tests_samples/COCO/000000039769.png",
											]






							def UpperCAmelCase_      (       self       ,   __snake_case       ,   __snake_case    ):
											pass






							@require_tf
							@unittest.skip("""Image segmentation not implemented in TF"""    )
							def UpperCAmelCase_      (       self    ):
											pass






							@slow
							@require_torch
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= pipeline("""mask-generation"""       ,   model="""facebook/sam-vit-huge"""    )

											_SCREAMING_SNAKE_CASE					:			Dict							= image_segmenter("""http://images.cocodataset.org/val2017/000000039769.jpg"""       ,   points_per_batch=256    )

											# Shortening by hashing
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for i, o in enumerate(outputs["""masks"""]    ):
															new_outupt += [{"mask": mask_to_test_readable(__snake_case    ), "scores": outputs["scores"][i]}]

											# fmt: off
											self.assertEqual(
											    nested_simplify(__snake_case       ,   decimals=4    )       ,   [
											        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
											        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.021},
											        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
											        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
											        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
											        {"""mask""": {"""hash""": """e2d0b7a0b7""", """shape""": (480, 640)}, """scores""": 0.9967},
											        {"""mask""": {"""hash""": """453c7844bd""", """shape""": (480, 640)}, """scores""": 0.993},
											        {"""mask""": {"""hash""": """3d44f2926d""", """shape""": (480, 640)}, """scores""": 0.9909},
											        {"""mask""": {"""hash""": """64033ddc3f""", """shape""": (480, 640)}, """scores""": 0.9879},
											        {"""mask""": {"""hash""": """801064ff79""", """shape""": (480, 640)}, """scores""": 0.9834},
											        {"""mask""": {"""hash""": """6172f276ef""", """shape""": (480, 640)}, """scores""": 0.9716},
											        {"""mask""": {"""hash""": """b49e60e084""", """shape""": (480, 640)}, """scores""": 0.9612},
											        {"""mask""": {"""hash""": """a811e775fd""", """shape""": (480, 640)}, """scores""": 0.9599},
											        {"""mask""": {"""hash""": """a6a8ebcf4b""", """shape""": (480, 640)}, """scores""": 0.9552},
											        {"""mask""": {"""hash""": """9d8257e080""", """shape""": (480, 640)}, """scores""": 0.9532},
											        {"""mask""": {"""hash""": """32de6454a8""", """shape""": (480, 640)}, """scores""": 0.9516},
											        {"""mask""": {"""hash""": """af3d4af2c8""", """shape""": (480, 640)}, """scores""": 0.9499},
											        {"""mask""": {"""hash""": """3c6db475fb""", """shape""": (480, 640)}, """scores""": 0.9483},
											        {"""mask""": {"""hash""": """c290813fb9""", """shape""": (480, 640)}, """scores""": 0.9464},
											        {"""mask""": {"""hash""": """b6f0b8f606""", """shape""": (480, 640)}, """scores""": 0.943},
											        {"""mask""": {"""hash""": """92ce16bfdf""", """shape""": (480, 640)}, """scores""": 0.943},
											        {"""mask""": {"""hash""": """c749b25868""", """shape""": (480, 640)}, """scores""": 0.9408},
											        {"""mask""": {"""hash""": """efb6cab859""", """shape""": (480, 640)}, """scores""": 0.9335},
											        {"""mask""": {"""hash""": """1ff2eafb30""", """shape""": (480, 640)}, """scores""": 0.9326},
											        {"""mask""": {"""hash""": """788b798e24""", """shape""": (480, 640)}, """scores""": 0.9262},
											        {"""mask""": {"""hash""": """abea804f0e""", """shape""": (480, 640)}, """scores""": 0.8999},
											        {"""mask""": {"""hash""": """7b9e8ddb73""", """shape""": (480, 640)}, """scores""": 0.8986},
											        {"""mask""": {"""hash""": """cd24047c8a""", """shape""": (480, 640)}, """scores""": 0.8984},
											        {"""mask""": {"""hash""": """6943e6bcbd""", """shape""": (480, 640)}, """scores""": 0.8873},
											        {"""mask""": {"""hash""": """b5f47c9191""", """shape""": (480, 640)}, """scores""": 0.8871}
											    ]       ,   )
											# fmt: on






							@require_torch
							@slow
							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= """facebook/sam-vit-huge"""
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= pipeline("""mask-generation"""       ,   model=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			int							= image_segmenter(
											    """http://images.cocodataset.org/val2017/000000039769.jpg"""       ,   pred_iou_thresh=1       ,   points_per_batch=256    )

											# Shortening by hashing
											_SCREAMING_SNAKE_CASE					:			List[str]							= []
											for i, o in enumerate(outputs["""masks"""]    ):
															new_outupt += [{"mask": mask_to_test_readable(__snake_case    ), "scores": outputs["scores"][i]}]

											self.assertEqual(
											    nested_simplify(__snake_case       ,   decimals=4    )       ,   [
											        {"""mask""": {"""hash""": """115ad19f5f""", """shape""": (480, 640)}, """scores""": 1.0444},
											        {"""mask""": {"""hash""": """6affa964c6""", """shape""": (480, 640)}, """scores""": 1.0210},
											        {"""mask""": {"""hash""": """dfe28a0388""", """shape""": (480, 640)}, """scores""": 1.0167},
											        {"""mask""": {"""hash""": """c0a5f4a318""", """shape""": (480, 640)}, """scores""": 1.0132},
											        {"""mask""": {"""hash""": """fe8065c197""", """shape""": (480, 640)}, """scores""": 1.0053},
											    ]       ,   )








'''simple docstring'''


import numpy as np





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= (0, 0)
											_SCREAMING_SNAKE_CASE					:			List[str]							= None
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
											_SCREAMING_SNAKE_CASE					:			List[str]							= 0
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0






							def __eq__(       self       ,   __snake_case    ):
											return self.position == cell.position






							def UpperCAmelCase_      (       self    ):
											print(self.position    )





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case=(5, 5)    ):
											_SCREAMING_SNAKE_CASE					:			str							= np.zeros(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= world_size[0]
											_SCREAMING_SNAKE_CASE					:			Tuple							= world_size[1]






							def UpperCAmelCase_      (       self    ):
											print(self.w    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[1]
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for n in neughbour_cord:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= current_x + n[0]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= current_y + n[1]
															if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= Cell()
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (x, y)
																			_SCREAMING_SNAKE_CASE					:			Dict							= cell
																			neighbours.append(__snake_case    )
											return neighbours







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				_open.append(SCREAMING_SNAKE_CASE__	)

				while _open:
								_SCREAMING_SNAKE_CASE					:			Any							= np.argmin([n.f for n in _open]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= _open[min_f]
								_closed.append(_open.pop(SCREAMING_SNAKE_CASE__	)	)
								if current == goal:
												break
								for n in world.get_neigbours(SCREAMING_SNAKE_CASE__	):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= current.g + 1
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= n.position
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= goal.position
												_SCREAMING_SNAKE_CASE					:			Dict							= (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE					:			Any							= n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= []
				while current.parent is not None:
								path.append(current.position	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= current.parent
				path.append(current.position	)
				return path[::-1]


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						Gridworld()
				# Start position and goal
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(0, 0)
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(4, 4)
				print(F"path from {start.position} to {goal.position}")
				UpperCAmelCase_						:  Union[str, Any]				  =						astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCAmelCase_						:  Optional[int]				  =						1
				print(world.w)




